Parking demand management systems are used to determine whether one or more vehicles are occupying a predetermined space. For example, parking demand management systems may make on-street parking occupancy determinations, parking lot occupancy determinations, and parking garage occupancy determinations. Such systems may use vision-based object (e.g., vehicle) detection as a step in the determination process. Vision-based object detection achieves accurate results with proper training and application of object classifiers.
To achieve high accuracy in vehicle detection, the classifier is applied to the same scenarios for which it is trained. For example, the classifier may be trained for detection at a particular site, during particular conditions (e.g., clear skies, daytime etc.), with a particular camera position. This is referred to as the source domain. The classifier may then be applied at the same site, during the same conditions, with the same camera position, to yield accurate vehicle detection results. This is referred to as the target domain.
This constraint in training and applying is often referred to as same domain application (i.e., source domain=target domain). When the site or conditions of the target domain changes (i.e., source domain target domain), the classifier needs to be either re-trained or domain adaptation is required to maintain accurate performance. Re-training is an effective way to maintain accurate performance if the user has sufficient time to collect and label a plurality of samples. Domain adaptation is a method to reduce the effort needed for re-training. What is needed, therefore, is an improved system and method for object detection when the target domain is different from the source domain.